Search Memory AI-powered semantic search across your personal knowledge base using Nowledge Mem. When to Use Strong signals to search: Continuity: Current topic connects to prior work Pattern match: Problem resembles past solved issue Decision context: "Why/how we chose X" implies documented rationale Recurring theme: Topic discussed in past sessions Implicit recall: "that approach", "like before" Contextual signals: Complex debugging (may match past root causes) Architecture discussion (choices may be documented) Domain-specific question (conventions likely stored) Skip when: Fundamentally new topic Generic syntax questions Fresh perspective explicitly requested Prerequisites nmem CLI - Choose one option: Option 1: uvx (Recommended)
Install uv if needed
curl -LsSf https://astral.sh/uv/install.sh | sh
Run nmem directly (auto-downloads)
uvx --from nmem-cli nmem --version Option 2: pip pip install nmem-cli nmem --version Ensure Nowledge Mem server is running at http://localhost:14242 Usage Use nmem CLI with --json flag for programmatic search:
Basic search
nmem --json m search "your query here"
With importance filter
nmem --json m search "API design" --importance 0.8
With labels (multiple labels use AND logic)
nmem --json m search "authentication" -l backend -l security
With time filter
nmem --json m search "meeting notes" -t week
Limit results
- nmem
- --json
- m search
- "debugging tips"
- -n
- 5
- Query Guidelines
- Extract semantic core from user's request
- Preserve domain terminology
- Multi-language aware (works with any language)
- Use 3-7 core concepts for best results
- Available Filters
- Flag
- Description
- Example
- --importance MIN
- Minimum importance (0.0-1.0)
- --importance 0.7
- -l, --label LABEL
- Filter by label (repeatable)
- -l frontend -l react
- -t, --time RANGE
- Time filter
- -t today
- ,
- -t week
- ,
- -t month
- -n NUM
- Limit results
- -n 5
- --unit-type TYPE
- Filter by memory type
- --unit-type decision
- Available unit types:
- fact
- ,
- preference
- ,
- decision
- ,
- plan
- ,
- procedure
- ,
- learning
- ,
- context
- ,
- event
- .
- Understanding Results
- Parse the
- memories
- array from JSON response. Check
- score
- field:
- 0.6-1.0
-
- Directly relevant - include in response
- 0.3-0.6
-
- Related context - may be useful
- < 0.3
- Skip - not relevant enough
Results may include a
source_thread
field linking the memory to the conversation it was distilled from. Use
nmem --json t show
to fetch the full conversation for deeper context. Response Guidelines Found relevant memories: Synthesize insights, cite when helpful No results: State clearly, suggest distilling current discussion if valuable Examples
Search for React patterns
nmem --json m search "React hooks patterns" -l frontend
Find debugging solutions
nmem --json m search "memory leak debugging" --importance 0.6
Recent project decisions
nmem --json m search "architecture decision" -t month -n 10 Links Documentation Nowledge Mem Discord Community